Interplanetary Network ANN Routing for Delay Minimization
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Solution Overview
Problem
Interplanetary communications networks face challenges in maintaining end-to-end connectivity and optimal routing due to the dynamic and time-dependent nature of node locations and propagation delays, which are not adequately addressed by traditional routing algorithms like Bellman-Ford and Dijkstra's algorithms, especially in the context of long-distance space communications where storage capacity is limited and data transfer windows are short.
Innovation Solution
An interplanetary communications network utilizing Artificial Neural Networks (ANNs) at each node for real-time estimation of path metrics and dynamic routing, allowing for time-dependent link state or hop-by-hop distance vector optimization to identify optimal paths and minimize delays, while also predicting node reconnections and handling storage and link costs effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional routing algorithms (Bellman-Ford, Dijkstra) are used, then routing computation is simpler, but end-to-end connectivity and routing optimality cannot be maintained due to dynamic node locations and time-dependent propagation delays
Solution Approach 1:
The patent implements dynamic routing by having each node continuously update its routing table based on real-time network conditions. The routing algorithm adapts to changing node locations and propagation delays by recalculating paths when link costs change, ensuring end-to-end connectivity is maintained despite the dynamic interplanetary environment.
Solution Approach 2:
The patent employs feedback mechanisms where nodes exchange routing information and propagate delay values through the network. Each node uses received feedback about link costs and propagation delays to dynamically adjust its routing decisions, creating a closed-loop system that maintains optimality in changing conditions.
2Reliability
If data is stored at intermediate nodes to handle intermittent connectivity, then data delivery reliability improves, but storage capacity is consumed and link costs increase
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing routing paths and propagation delay information at each node before data transmission is needed. Nodes maintain updated routing tables that predict optimal paths, allowing data to be forwarded reliably without requiring extensive intermediate storage capacity.
Solution Approach 2:
The patent introduces routing information and propagation delay data as intermediary elements that mediate between source and destination nodes. These intermediaries carry path optimization information, enabling reliable data delivery through dynamic path selection rather than through storage buffering at intermediate nodes.
3Productivity
If routing paths are optimized for minimal delay, then data transfer efficiency improves, but the system becomes more sensitive to changes in node locations and network conditions
Solution Approach 1:
The patent achieves both optimization and adaptability through dynamic routing tables that are continuously updated. The system optimizes for minimal delay by selecting paths with lowest propagated delay values, while simultaneously adapting to changing conditions by recalculating routes when node locations or link costs change, making the optimization itself dynamic rather than static.
Solution Approach 2:
The patent merges the objectives of delay optimization and adaptability by combining real-time propagation delay measurements with dynamic path selection. The routing algorithm simultaneously considers current network state for optimization and incorporates mechanisms to detect and respond to changes, unifying both goals in a single adaptive optimization process.
Data Source
AI summary
An interplanetary communications network, an interplanetary communications backbone network of Artificial Neural Network (ANN) nodes, an ANN node and a method of managing interplanetary communications. The backbone network operates as a neural network with each node identifying optimum paths, e.g., end-to-end through the backbone network from a distant planet to an earth node. Each node maintains a window matrix identifying reoccurring (e.g., periodically) communications windows between nodes and a propagation delay matrix identifying time varying propagation delays between nodes. Each node determines whether and how long to store packets locally to minimize path delays. Each node also maintains a link cost matrix indicating the cost of links to neighboring nodes and further determines whether and how long to store packets locally to minimize path delays at minimal link cost.